Large-scale data processing may include extracting data of interest from raw data in one or more databases and processing it into a data product. For example, regression analysis may be conducted based on a very large dataset and includes statistical processes for estimating the relationships among variables. It may be used to predict or forecast a given action or event and may be based on analyzing historical or test data containing variables that contribute to the prediction and forecasting. As a specific example, large-scale machine learning systems may process large amounts of training data from data streams received by the system. A data stream may include training examples corresponding to specific instances of an event or action such as when a user selects a specific search result, or when a single video is viewed from among multiple videos presented to a user. An example may contain features (i.e., observed properties such as a user being located in the USA, a user preferring to speak English, etc.) and may also contain a label which may indicate an event or action associated with the example (e.g., a user selected a specific search result, a user did not select a specific search result, a user viewed a particular video, etc.). These examples may be used to generate statistics for each of the features and these statistics may be used to generate a model. As a result, a machine learning system may use this model to make predictions.